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Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 10)
Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 10)
Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 10)
Discover what you can do with R! This book guides users through practical, powerful tools for hands-on analysis and interpretation of data.
Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 10)
Item #: 30708971

Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 10)

Item #: 30708971

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    Discover what you can do with R! This book guides users through practical, powerful tools for hands-on analysis and interpretation of data.
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    What Stands Out

    Example-Based Learning
    Offers practical, example-driven learning that makes complex concepts accessible, enhancing understanding and retention for students and professionals alike.
    Comprehensive Coverage
    Thoroughly covers data analysis techniques and graphics using R, equipping readers with essential skills needed for real-world data scenarios and problem-solving.
    Updated Content
    Includes the latest advancements in R and statistical methods, ensuring readers stay current in the rapidly evolving field of data science.

    Product Details

    Explore the Example-Based Approach of Data Analysis and Graphics Using R. Discover how to apply statistical and probabilistic mathematics through Cambridge Series Number 10. Shop at Ubuy UAE
    • Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practicing statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
    Publisher Cambridge University Press
    Publication date June 7, 2010
    Edition 3rd
    Language English
    Print length 549 pages
    ISBN-10 0521762936
    ISBN-13 978-0521762939
    Item Weight 2.87 pounds (1.3 kg)
    Dimensions 7.25 x 1.25 x 10 inches (18.4 x 3.2 x 25.4 cm)
    Part of series Cambridge Series in Statistical and Probabilistic Mathematics

    Who Should Buy?

    Suitable For
    • Students and Learners

      Perfect for students aiming to solidify their understanding of data analysis using R through practical examples.

    • Data Analysts

      Ideal for professionals who need to enhance their data visualization skills and apply statistical techniques effectively.

    • Teachers and Educators

      Beneficial for educators looking for comprehensive teaching materials and examples to guide students in data analysis.

    Not Suitable For
    • Complete Beginners

      Not suitable for individuals with no prior experience in R or data analysis concepts, as it assumes some knowledge.

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    Biostatistics Editorial Review

    This book is highly recommended for anyone looking to learn R and statistics. It provides useful code examples and highlights common pitfalls for new users. The included website allows for easy access to downloadable code. While there are free resources available, this hard-bound reference book offers a comprehensive and organized approach to learning R. It covers a range of statistical applications and provides value even if only a few chapters are utilized.

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    Pros

    • Useful for learning R
    • Includes code examples
    • Highlights common pitfalls
    • Comprehensive reference
    • Covers range of statistical applications

    Cons

    • Not focused solely on statistics

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